Classification of humus forms in Caspian Hyrcanian mixed forests ecoregion (Iran): Comparison between two classification methods

Abstract A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phaseLC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis tookonly 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil wereused: one input-class, two input-class, and pseudo two input-class.

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Klasifikasi Motif Batik Berbasis Kemiripan Ciri dengan Wavelet Transform dan Fuzzy Neural Network

ComTech Computer Mathematics and Engineering Applications ◽

10.21512/comtech.v5i1.2630 ◽

2014 ◽

Vol 5 (1) ◽

pp. 361

Author(s):

A Haris Rangkuti

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Fuzzy Neural Network ◽

Classification Methods ◽

Neural Network Classification ◽

Fuzzy Input ◽

Fuzzy Neural ◽

Input Variables

This paper introduces a classification of the image of the batik process, which is based on the similarity of the characteristics, by combining the method of wavelet transform Daubechies type 2 level 2, to process the characteristic texture consisting of standard deviation, mean and energy as input variables, using the method of Fuzzy Neural Network (FNN). Fuzzyfikasi process will be carried out all input values with five categories: Very Low (VL), Low (L), Medium (M), High (H) and Very High (VH). The result will be a fuzzy input in the process of neural network classification methods. The result will be a fuzzy input in the process of neural network classification methods. For the image to be processed seven types of batik motif is ceplok, kawung, lereng, parang, megamendung, tambal and nitik. The results of the classification process with FNN is rule generation, so for the new image of batik can be immediately known motif types after treatment with FNN classification. For the degree of precision of this method is 86-92%.

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A Study on Classification Algorithm of Rectangle Curved Hull Plates for Plate Fabrication

Journal of Ship Production and Design ◽

10.5957/jspd.2016.32.3.166 ◽

2016 ◽

Vol 32 (03) ◽

pp. 166-173

Author(s):

ChanSuk Kim ◽

Jong Gye Shin ◽

Eungkon Kim ◽

YangRyul Choi

Keyword(s):

Classification Algorithm ◽

Classification Methods ◽

Boundary Curvature

Since a ship's hull consists of various curved plates, different fabrication methods are applied for efficient fabrication works of curved hull plates. Currently, the classification methods largely rely on division resolution and thus lead to insufficient reliability. This article proposes four standard shapes for fabrication by calculating boundary curvature of each curved plate so that same curvature areas could be acquired. Some examples are carried out for the classification of curved hull plates.

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Comparative analysis of damage to sewer rehabilitation coatings

E3S Web of Conferences ◽

10.1051/e3sconf/20184500041 ◽

2018 ◽

Vol 45 ◽

pp. 00041

Author(s):

Andrzej Kuliczkowski ◽

Stanisław Nogaj

Keyword(s):

Damage Assessment ◽

Technical Condition ◽

Classification Methods ◽

Damage Classification ◽

Reliable Assessment ◽

Sewer Rehabilitation ◽

Underground Infrastructure ◽

Original Part ◽

Near Future

Technologies for the trenchless rehabilitation of pipelines using various types of coatings have been used for almost half a century. Considering that the assumed life expectancy of such renewed pipelines is 50 years, it will be necessary to assess their technical condition in the near future. The aim of this article is to attempt to answer the question "Do existing damage classification methods allow for the full and reliable assessment of the sewers already renewed with rehabilitation coatings?". The scope of the article, and its original part, is to describe how the problem of damage assessment of rehabilitation coatings has been included in various methods of classification of underground infrastructure pipelines, and conducting a comparison that compares these methods in terms of the damages described. An interpretation of the results of the research on rehabilitation coatings operated in various time periods, starting from those recently applied to those operating for over 30 years, was also made. The result of the analysis is to present the differences and deficiencies in the damage classification methods discussed.

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Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar

Remote Sensing ◽

10.3390/rs11040405 ◽

2019 ◽

Vol 11 (4) ◽

pp. 405

Author(s):

Xuan Feng ◽

Haoqiu Zhou ◽

Cai Liu ◽

Yan Zhang ◽

Wenjing Liang ◽

...

Keyword(s):

Ground Penetrating Radar ◽

Decomposition Methods ◽

Support Vector ◽

Classification Methods ◽

Target Classification ◽

Data Set ◽

Particle Center ◽

Ground Penetrating ◽

New Criterion

The subsurface target classification of ground penetrating radar (GPR) is a popular topic in the field of geophysics. Among the existing classification methods, geometrical features and polarimetric attributes of targets are primarily used. As polarimetric attributes contain more information of targets, polarimetric decomposition methods, such as H-Alpha decomposition, have been developed for target classification of GPR in recent years. However, the classification template used in H-Alpha classification is preset depending on the experience of synthetic aperture radar (SAR); therefore, it may not be suitable for GPR. Moreover, many existing classification methods require excessive human operation, particularly when outliers exist in the sample (the data set containing the features of targets); therefore, they are not efficient or intelligent. We herein propose a new machine learning method based on sample centers, i.e., particle center supported plane (PCSP). The sample center is defined as the point with the smallest sum of distances from all points in the same sample, which is considered as a better representation of the sample without significant effect of the outliers. In this proposed method, particle swarm optimization (PSO) is performed to obtain the sample centers; the new criterion for subsurface target classification is achieved. We applied this algorithm to full polarimetric GPR data measured in the laboratory and outdoors. The results indicate that, comparing with support vector machine (SVM) and classical H-Alpha classification, this new method is more efficient and the accuracy is relatively high.

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Identification of Heracleum sosnowskyi-Invaded Land Using Earth Remote Sensing Data

Sustainability ◽

10.3390/su12030759 ◽

2020 ◽

Vol 12 (3) ◽

pp. 759

Author(s):

Jūratė Sužiedelytė Visockienė ◽

Eglė Tumelienė ◽

Vida Maliene

Keyword(s):

Remote Sensing ◽

Invasive Plant ◽

Remote Sensing Data ◽

Unsupervised Classification ◽

Economic Losses ◽

Classification Methods ◽

Earth Remote Sensing ◽

Heracleum Sosnowskyi

H. sosnowskyi (Heracleum sosnowskyi) is a plant that is widespread both in Lithuania and other countries and causes abundant problems. The damage caused by the population of the plant is many-sided: it menaces the biodiversity of the land, poses risk to human health, and causes considerable economic losses. In order to find effective and complex measures against this invasive plant, it is very important to identify places and areas where H. sosnowskyi grows, carry out a detailed analysis, and monitor its spread to avoid leaving this process to chance. In this paper, the remote sensing methodology was proposed to identify territories covered with H. sosnowskyi plants (land classification). Two categories of land cover classification were used: supervised (human-guided) and unsupervised (calculated by software). In the application of the supervised method, the average wavelength of the spectrum of H. sosnowskyi was calculated for the classification of the RGB image and according to this, the unsupervised classification by the program was accomplished. The combination of both classification methods, performed in steps, allowed obtaining better results than using one. The application of authors’ proposed methodology was demonstrated in a Lithuanian case study discussed in this paper.

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A European morpho-functional classification of humus forms

Geoderma ◽

10.1016/j.geoderma.2011.05.016 ◽

2011 ◽

Vol 164 (3-4) ◽

pp. 138-145 ◽

Cited By ~ 107

Author(s):

A. Zanella ◽

B. Jabiol ◽

J.F. Ponge ◽

G. Sartori ◽

R. De Waal ◽

...

Keyword(s):

Functional Classification ◽

Humus Forms

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Classification of traumatic brain injury severity using retrospective data

Journal of Nursing Education and Practice ◽

10.5430/jnep.v7n11p23 ◽

2017 ◽

Vol 7 (11) ◽

pp. 23

Author(s):

Sandra Rogers ◽

Amber W. Trickey

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Motor Vehicle ◽

The United States ◽

Retrospective Data ◽

Trauma Centers ◽

Classification Methods ◽

Severity Classification ◽

Brain Injured

Objective: Accurate classification of traumatic brain injury (TBI) severity is essential to brain injury research. TBI heterogeneity complicates classification of the injury; is a significant barrier in the design of therapeutic interventions; and results in retrospective data which is difficult to translate. The objective of this study is to describe the differences in two current tools used in the classification of TBI severity, the Glasgow Coma Scale (GCS) and the head Abbreviated Injury Score (AIS), using retrospective data to compare their performance.Methods: Using correlational and descriptive statistics, this study examined two TBI severity classification methods across a large sample of TBI patients (N = 56,131), who were treated at level I and level II trauma centers in the United States and were included in the 2010 National Sample Program (NSP) of the National Trauma Data Bank (NTDB®).Results: The study population was 67% male, 67% non-Hispanic white, treated most often in trauma centers in the South (38%), with blunt trauma (93%) and from non-motor vehicle collisions (MVC’s) (56%). Observation of the AIS classification system demonstrated that it tends to over-score TBI severity compared to the GCS classification. The methods (GCS & AIS) had a weak, inverse relationship with a correlation coefficient (Pearson’s r) of -0.3980, which was significant at p < .001.Conclusions: The current study addressed the difficulties associated with categorizing TBI severity when analyzing retrospective data. Although AIS is commonly used to classify severity in retrospective data when GCS is unavailable, the relationship between the two scales is relatively unknown. Results show that AIS and GCS are more closely related for severely brain injured patients but in cases of mild and moderate injury, AIS is less predictive of GCS. Since they are often used in conjunction in identifying brain injured severity in retrospective data, researchers cannot be certain that the tools are similarly classifying mild, moderate, and severe injuries. This study reinforces the need for additional TBI severity classification methods, such as neuroimaging techniques and biomarkers.

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PERBANDINGAN ANTARA METODE CART (CLASSIFICATION AND EGRESSION TREE) DAN REGRESI LOGISTIK (LOGISTIC REGRESSION) DALAM MENGKLASIFIKASIKAN PASIEN PENDERITA DBD (DEMAM BERDARAH DENGUE)

JURNAL ILMIAH MATEMATIKA DAN TERAPAN ◽

10.22487/2540766x.2018.v15.i1.10206 ◽

2018 ◽

Vol 15 (1) ◽

pp. 98-107

Author(s):

R Lestawati ◽

Rais Rais ◽

I T Utami

Keyword(s):

Logistic Regression ◽

Statistical Methods ◽

Parametric Method ◽

Parametric Methods ◽

Classification Methods ◽

Classification Result ◽

The Status ◽

Classification Table ◽

Non Parametric

Classification is one of statistical methods in grouping the data compiled systematically. The classification of an object can be done by two approaches, namely classification methods parametric and non-parametric methods. Non-parametric methods is used in this study is the method of CART to be compared to the classification result of the logistic regression as one of a parametric method. From accuracy classification table of CART method to classify the status of DHF patient into category of severe and non-severe exactly 76.3%, whereas the percentage of truth logistic regression was 76.7%, CART method to classify the status of DHF patient into categories of severe and non-severe exactly 76.3%, CART method yielded 4 significant variables that hepatomegaly, epitaksis, melena and diarrhea as well as the classification is divided into several segmens into a more accurate whereas the logistic regression produces only 1 significant variables that hepatomegaly

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A standardized, extensible framework for optimizing classification improves marker-gene taxonomic assignments

10.7287/peerj.preprints.934v1 ◽

2015 ◽

Cited By ~ 1

Author(s):

Nicholas A Bokulich ◽

Jai Ram Rideout ◽

Evguenia Kopylova ◽

Evan Bolyen ◽

Jessica Patnode ◽

...

Keyword(s):

Marker Gene ◽

Amplicon Sequencing ◽

Evaluation Framework ◽

Taxonomic Resolution ◽

Marker Genes ◽

Classification Methods ◽

New Methods ◽

Taxonomic Assignments ◽

Different Levels

Background: Taxonomic classification of marker-gene (i.e., amplicon) sequences represents an important step for molecular identification of microorganisms. Results: We present three advances in our ability to assign and interpret taxonomic classifications of short marker gene sequences: two new methods for taxonomy assignment, which reduce runtime up to two-fold and achieve high precision genus-level assignments; an evaluation of classification methods that highlights differences in performance with different marker genes and at different levels of taxonomic resolution; and an extensible framework for evaluating and optimizing new classification methods, which we hope will serve as a model for standardized and reproducible bioinformatics methods evaluations. Conclusions: Our new methods are accessible in QIIME 1.9.0, and our evaluation framework will support ongoing optimization of classification methods to complement rapidly evolving short-amplicon sequencing and bioinformatics technologies. Static versions of all of the analysis notebooks generated with this framework, which contain all code and analysis results, can be viewed at http://bit.ly/srta-010.

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